Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Daniel Felix Ritchie School of Engineering and Computer Science (28)
- Electrical and Computer Engineering (12)
- Artificial intelligence (6)
- Computer Science (6)
- Machine learning (6)
-
- Facial expression recognition (4)
- Mechanical and Materials Engineering (3)
- Abstracts (2)
- Computer vision (2)
- Deep neural networks (2)
- Reinforcement learning (2)
- Undergraduate research (2)
- Ad-Corre loss (1)
- Adversary aware navigation (1)
- Affect perception (1)
- Autonomous robot navigation (1)
- Bias mitigation methods (1)
- Binocular combination (1)
- Biomechanics (1)
- Body model (1)
- Bounding boxes (1)
- Building energy (1)
- CUDA (1)
- Canonical theory (1)
- Capillary electrophoresis (1)
- Chinese cyber policies (1)
- Circulation control (1)
- Collision detection (1)
- Communication delay variability (1)
- Computer engineering (1)
- Publication Year
- Publication
- Publication Type
Articles 1 - 30 of 30
Full-Text Articles in Engineering
Terrain And Adversary-Aware Autonomous Robot Navigation, Aniekan Ufot Inyang
Terrain And Adversary-Aware Autonomous Robot Navigation, Aniekan Ufot Inyang
Electronic Theses and Dissertations
In autonomous robot navigation, the robot is able to understand the environment around it for intelligent navigation. From its world model of this environment, it generates a global plan for navigation from a position to a goal based on different factors. This research aims to implement autonomous robot navigation by learning terrain affordances: traversability (moving quickly) and concealment (staying hidden from an adversary) using the Preference-based Inverse Reward Learning (PbIRL) methodology. The PbIRL methodology reduces the barrier of generating initial demonstration data to learn the terrain affordances by using a human expert’s preferences to learn individual weights over the terrain …
Controllable Language Generation Using Deep Learning, Rohola Zandie
Controllable Language Generation Using Deep Learning, Rohola Zandie
Electronic Theses and Dissertations
The advent of deep neural networks has sparked a revolution in Artificial Intelligence (AI), notably with the creation of Transformer models like GPT-X and ChatGPT. These models have surpassed previous methods in various Natural Language Processing (NLP) tasks. As the NLP field evolves, there is a need to further understand and question the capabilities of these models. Text generation, a crucial part of NLP, remains an area where our comprehension is limited while being critical in research.
This dissertation focuses on the challenging problem of controlling the general behaviors of language models such as sentiment, topical focus, and logical reasoning. …
Patient Movement Monitoring Based On Imu And Deep Learning, Mohsen Sharifi Renani
Patient Movement Monitoring Based On Imu And Deep Learning, Mohsen Sharifi Renani
Electronic Theses and Dissertations
Osteoarthritis (OA) is the leading cause of disability among the aging population in the United States and is frequently treated by replacing deteriorated joints with metal and plastic components. Developing better quantitative measures of movement quality to track patients longitudinally in their own homes would enable personalized treatment plans and hasten the advancement of promising new interventions. Wearable sensors and machine learning used to quantify patient movement could revolutionize the diagnosis and treatment of movement disorders. The purpose of this dissertation was to overcome technical challenges associated with the use of wearable sensors, specifically Inertial Measurement Units (IMUs), as a …
Novel Approach For Non-Invasive Prediction Of Body Shape And Habitus, Emma Young
Novel Approach For Non-Invasive Prediction Of Body Shape And Habitus, Emma Young
Electronic Theses and Dissertations
While marker-based motion capture remains the gold standard in measuring human movement, accuracy is influenced by soft-tissue artifacts, particularly for subjects with high body mass index (BMI) where markers are not placed close to the underlying bone. Obesity influences joint loads and motion patterns, and BMI may not be sufficient to capture the distribution of a subject’s weight or to differentiate differences between subjects. Subjects in need of a joint replacement are more likely to have mobility issues or pain, which prevents exercise. Obesity also increases the likelihood of needing a total joint replacement. Accurate movement data for subjects with …
Du Undergraduate Showcase: Research, Scholarship, And Creative Works, Caitlyn Aldersea, Justin Bravo, Sam Allen, Anna Block, Connor Block, Emma Buechler, Maria De Los Angeles Bustillos, Arianna Carlson, William Christensen, Olivia Kachulis, Noah Craver, Kate Dillon, Muskan Fatima, Angel Fernandes, Emma Finch, Colleen Cassidy, Amy Fishman, Andrea Francis, Stacia Fritz, Simran Gill, Emma Gries, Rylie Hansen, Shannon Powers, Jacqueline Martinez, Zachary Harker, Ashley Hasty, Mykaela Tanino-Springsteen, Kathleen Hopps, Adelaide Kerenick, Colin Kleckner, Ci Koehring, Elijah Kruger, Braden Krumholz, Maddie Leake, Lyneé Alves, Seraphina Loukas, Yatzari Lozano Vazquez, Haley Maki, Emily Martinez, Sierra Mckinney, Mykaela Tanino-Springsteen, Audrey Mitchell, Kipling Newman, Audrey Ng, Megan Lucyshyn, Andrew Nguyen, Stevie Ostman, Casandra Pearson, Alexandra Penney, Julia Gielczynski, Tyler Ball, Anna Rini, Christina Rorres, Simon Ruland, Helayna Schafer, Emma Sellers, Sarah Schuller, Claire Shaver, Kevin Summers, Isabella Shaw, Madison Sinar, Claudia Pena, Apshara Siwakoti, Carter Sorensen, Madi Sousa, Anna Sparling, Alexandra Revier, Brandon Thierry, Dylan Tyree, Maggie Williams, Lauren Wols
Du Undergraduate Showcase: Research, Scholarship, And Creative Works, Caitlyn Aldersea, Justin Bravo, Sam Allen, Anna Block, Connor Block, Emma Buechler, Maria De Los Angeles Bustillos, Arianna Carlson, William Christensen, Olivia Kachulis, Noah Craver, Kate Dillon, Muskan Fatima, Angel Fernandes, Emma Finch, Colleen Cassidy, Amy Fishman, Andrea Francis, Stacia Fritz, Simran Gill, Emma Gries, Rylie Hansen, Shannon Powers, Jacqueline Martinez, Zachary Harker, Ashley Hasty, Mykaela Tanino-Springsteen, Kathleen Hopps, Adelaide Kerenick, Colin Kleckner, Ci Koehring, Elijah Kruger, Braden Krumholz, Maddie Leake, Lyneé Alves, Seraphina Loukas, Yatzari Lozano Vazquez, Haley Maki, Emily Martinez, Sierra Mckinney, Mykaela Tanino-Springsteen, Audrey Mitchell, Kipling Newman, Audrey Ng, Megan Lucyshyn, Andrew Nguyen, Stevie Ostman, Casandra Pearson, Alexandra Penney, Julia Gielczynski, Tyler Ball, Anna Rini, Christina Rorres, Simon Ruland, Helayna Schafer, Emma Sellers, Sarah Schuller, Claire Shaver, Kevin Summers, Isabella Shaw, Madison Sinar, Claudia Pena, Apshara Siwakoti, Carter Sorensen, Madi Sousa, Anna Sparling, Alexandra Revier, Brandon Thierry, Dylan Tyree, Maggie Williams, Lauren Wols
DU Undergraduate Research Journal Archive
DU Undergraduate Showcase: Research, Scholarship, and Creative Works
Design, Determination, And Evaluation Of Gender-Based Bias Mitigation Techniques For Music Recommender Systems, Sunny Shrestha
Design, Determination, And Evaluation Of Gender-Based Bias Mitigation Techniques For Music Recommender Systems, Sunny Shrestha
Electronic Theses and Dissertations
The majority of smartphone users engage with a recommender system on a daily basis. Many rely on these recommendations to make their next purchase, download the next game, listen to the new music or find the next healthcare provider. Although there are plenty of evidence backed research that demonstrates presence of gender bias in Machine Learning (ML) models like recommender systems, the issue is viewed as a frivolous cause that doesn’t merit much action. However, gender bias poses to effect more than half of the population as by default ML systems are designed to cater to a cisgender man. This …
Reference Frames In Human Sensory, Motor, And Cognitive Processing, Dongcheng He
Reference Frames In Human Sensory, Motor, And Cognitive Processing, Dongcheng He
Electronic Theses and Dissertations
Reference-frames, or coordinate systems, are used to express properties and relationships of objects in the environment. While the use of reference-frames is well understood in physical sciences, how the brain uses reference-frames remains a fundamental question. The goal of this dissertation is to reach a better understanding of reference-frames in human perceptual, motor, and cognitive processing. In the first project, we study reference-frames in perception and develop a model to explain the transition from egocentric (based on the observer) to exocentric (based outside the observer) reference-frames to account for the perception of relative motion. In a second project, we focus …
Artificial Emotional Intelligence In Socially Assistive Robots, Hojjat Abdollahi
Artificial Emotional Intelligence In Socially Assistive Robots, Hojjat Abdollahi
Electronic Theses and Dissertations
Artificial Emotional Intelligence (AEI) bridges the gap between humans and machines by demonstrating empathy and affection towards each other. This is achieved by evaluating the emotional state of human users, adapting the machine’s behavior to them, and hence giving an appropriate response to those emotions. AEI is part of a larger field of studies called Affective Computing. Affective computing is the integration of artificial intelligence, psychology, robotics, biometrics, and many more fields of study. The main component in AEI and affective computing is emotion, and how we can utilize emotion to create a more natural and productive relationship between humans …
Du Undergraduate Showcase: Research, Scholarship, And Creative Works: Abstracts, Emma Aggeler, Elena Arroway, Daisy T. Booker, Justin Bravo, Kyle Bucholtz, Megan Burnham, Nicole Choi, Spencer Cockerell, Rosie Contino, Jackson Garske, Kaitlyn Glover, Caroline Hamilton, Haley Hartmann, Madalyne Heiken, Colin Holter, Leah Huzjak, Alyssa Jeng, Cole Jernigan, Chad Kashiwa, Adelaide Kerenick, Emily King, Abigail Langeberg, Maddie Leake, Meredith Lemons, Alec Mackay, Greer Mckinley, Ori Miller, Guy Milliman, Katherine Miromonti, Audrey Mitchell, Lauren Moak, Megan Morrell, Gelella Nebiyu, Zdenek Otruba, Toni V. Panzera, Kassidy Patarino, Sneha Patil, Alexandra Penney, Kevin Persky, Caitlin Pham, Gabriela Recinos, Mary Ringgenberg, Chase Routt, Olivia Schneider, Roman Shrestha, Arlo Simmerman, Alec Smith, Tessa Smith, Nhi-Lac Thai, Kyle Thurmann, Casey Tindall, Amelia Trembath, Maria Trubetskaya, Zachary Vangelisti, Peter Vo, Abby Walker, David Winter, Grayden Wolfe, Leah York
Du Undergraduate Showcase: Research, Scholarship, And Creative Works: Abstracts, Emma Aggeler, Elena Arroway, Daisy T. Booker, Justin Bravo, Kyle Bucholtz, Megan Burnham, Nicole Choi, Spencer Cockerell, Rosie Contino, Jackson Garske, Kaitlyn Glover, Caroline Hamilton, Haley Hartmann, Madalyne Heiken, Colin Holter, Leah Huzjak, Alyssa Jeng, Cole Jernigan, Chad Kashiwa, Adelaide Kerenick, Emily King, Abigail Langeberg, Maddie Leake, Meredith Lemons, Alec Mackay, Greer Mckinley, Ori Miller, Guy Milliman, Katherine Miromonti, Audrey Mitchell, Lauren Moak, Megan Morrell, Gelella Nebiyu, Zdenek Otruba, Toni V. Panzera, Kassidy Patarino, Sneha Patil, Alexandra Penney, Kevin Persky, Caitlin Pham, Gabriela Recinos, Mary Ringgenberg, Chase Routt, Olivia Schneider, Roman Shrestha, Arlo Simmerman, Alec Smith, Tessa Smith, Nhi-Lac Thai, Kyle Thurmann, Casey Tindall, Amelia Trembath, Maria Trubetskaya, Zachary Vangelisti, Peter Vo, Abby Walker, David Winter, Grayden Wolfe, Leah York
DU Undergraduate Research Journal Archive
Abstracts from the DU Undergraduate Showcase.
Ad-Corre: Adaptive Correlation-Based Loss For Facial Expression Recognition In The Wild, Ali Pourramezan Fard, Mohammad H. Mahoor
Ad-Corre: Adaptive Correlation-Based Loss For Facial Expression Recognition In The Wild, Ali Pourramezan Fard, Mohammad H. Mahoor
Electrical and Computer Engineering: Faculty Scholarship
Automated Facial Expression Recognition (FER) in the wild using deep neural networks is still challenging due to intra-class variations and inter-class similarities in facial images. Deep Metric Learning (DML) is among the widely used methods to deal with these issues by improving the discriminative power of the learned embedded features. This paper proposes an Adaptive Correlation (Ad-Corre) Loss to guide the network towards generating embedded feature vectors with high correlation for within-class samples and less correlation for between-class samples. Ad-Corre consists of 3 components called Feature Discriminator, Mean Discriminator, and Embedding Discriminator. We design the Feature Discriminator component to guide …
Classification Of Electropherograms Using Machine Learning For Parkinson’S Disease, Soroush Dehghan
Classification Of Electropherograms Using Machine Learning For Parkinson’S Disease, Soroush Dehghan
Electronic Theses and Dissertations
Parkinson’s disease (PD) is a neurodegenerative movement disorder that progresses gradually over time. The onset of symptoms in people who are suffering from PD can vary from case to case, and it depends on the progression of the disease in each patient. The PD symptoms gradually develop and exacerbate the patient’s movements throughout time. An early diagnosis of PD could improve the outcomes of treatments and could potentially delay the progression of this disorder and that makes discovering a new diagnostic method valuable. In this study, I investigate the feasibility of using a machine learning (ML) approach to classify PD …
Wind Turbine Parameter Calibration Using Deep Learning Approaches, Rebecca Mccubbin
Wind Turbine Parameter Calibration Using Deep Learning Approaches, Rebecca Mccubbin
Electronic Theses and Dissertations
The inertia and damping coefficients are critical to understanding the workings of a wind turbine, especially when it is in a transient state. However, many manufacturers do not provide this information about their turbines, requiring people to estimate these values themselves. This research seeks to design a multilayer perceptron (MLP) that can accurately predict the inertia and damping coefficients using the power data from a turbine during a transient state. To do this, a model of a wind turbine was built in Matlab, and a simulation of a three-phase fault was used to collect realistic fault data to input into …
Deep Learning Methods For Fingerprint-Based Indoor And Outdoor Positioning, Fahad Alhomayani
Deep Learning Methods For Fingerprint-Based Indoor And Outdoor Positioning, Fahad Alhomayani
Electronic Theses and Dissertations
Outdoor positioning systems based on the Global Navigation Satellite System have several shortcomings that have deemed their use for indoor positioning impractical. Location fingerprinting, which utilizes machine learning, has emerged as a viable method and solution for indoor positioning due to its simple concept and accurate performance. In the past, shallow learning algorithms were traditionally used in location fingerprinting. Recently, the research community started utilizing deep learning methods for fingerprinting after witnessing the great success and superiority these methods have over traditional/shallow machine learning algorithms. The contribution of this dissertation is fourfold:
First, a Convolutional Neural Network (CNN)-based method for …
Satellite Constellation Deployment And Management, Joseph Ryan Kopacz
Satellite Constellation Deployment And Management, Joseph Ryan Kopacz
Electronic Theses and Dissertations
This paper will review results and discuss a new method to address the deployment and management of a satellite constellation. The first two chapters will explorer the use of small satellites, and some of the advances in technology that have enabled small spacecraft to maintain modern performance requirements in incredibly small packages.
The third chapter will address the multiple-objective optimization problem for a global persistent coverage constellation of communications spacecraft in Low Earth Orbit. A genetic algorithm was implemented in MATLAB to explore the design space – 288 trillion possibilities – utilizing the Satellite Tool Kit (STK) software developers kit. …
Deep Reinforcement Learning For The Optimization Of Building Energy Control And Management, Jun Hao
Deep Reinforcement Learning For The Optimization Of Building Energy Control And Management, Jun Hao
Electronic Theses and Dissertations
Most of the current game-theoretic demand-side management methods focus primarily on the scheduling of home appliances, and the related numerical experiments are analyzed under various scenarios to achieve the corresponding Nash-equilibrium (NE) and optimal results. However, not much work is conducted for academic or commercial buildings. The methods for optimizing academic-buildings are distinct from the optimal methods for home appliances. In my study, we address a novel methodology to control the operation of heating, ventilation, and air conditioning system (HVAC).
We assume that each building in our campus is equipped with smart meter and communication system which is envisioned in …
Facial Action Unit Detection With Deep Convolutional Neural Networks, Siddhesh Padwal
Facial Action Unit Detection With Deep Convolutional Neural Networks, Siddhesh Padwal
Electronic Theses and Dissertations
The facial features are the most important tool to understand an individual's state of mind. Automated recognition of facial expressions and particularly Facial Action Units defined by Facial Action Coding System (FACS) is challenging research problem in the field of computer vision and machine learning. Researchers are working on deep learning algorithms to improve state of the art in the area. Automated recognition of facial action units has man applications ranging from developmental psychology to human robot interface design where companies are using this technology to improve their consumer devices (like unlocking phone) and for entertainment like FaceApp. Recent studies …
Deep Siamese Neural Networks For Facial Expression Recognition In The Wild, Wassan Hayale
Deep Siamese Neural Networks For Facial Expression Recognition In The Wild, Wassan Hayale
Electronic Theses and Dissertations
The variation of facial images in the wild conditions due to head pose, face illumination, and occlusion can significantly affect the Facial Expression Recognition (FER) performance. Moreover, between subject variation introduced by age, gender, ethnic backgrounds, and identity can also influence the FER performance. This Ph.D. dissertation presents a novel algorithm for end-to-end facial expression recognition, valence and arousal estimation, and visual object matching based on deep Siamese Neural Networks to handle the extreme variation that exists in a facial dataset. In our main Siamese Neural Networks for facial expression recognition, the first network represents the classification framework, where we …
Automated Recognition Of Facial Affect Using Deep Neural Networks, Behzad Hasani
Automated Recognition Of Facial Affect Using Deep Neural Networks, Behzad Hasani
Electronic Theses and Dissertations
Automated Facial Expression Recognition (FER) has been a topic of study in the field of computer vision and machine learning for decades. In spite of efforts made to improve the accuracy of FER systems, existing methods still are not generalizable and accurate enough for use in real-world applications. Many of the traditional methods use hand-crafted (a.k.a. engineered) features for representation of facial images. However, these methods often require rigorous hyper-parameter tuning to achieve favorable results.
Recently, Deep Neural Networks (DNNs) have shown to outperform traditional methods in visual object recognition. DNNs require huge data as well as powerful computing units …
Real-Time Detection Of Demand Manipulation Attacks On A Power Grid, Srinidhi Madabhushi
Real-Time Detection Of Demand Manipulation Attacks On A Power Grid, Srinidhi Madabhushi
Electronic Theses and Dissertations
An increased usage in IoT devices across the globe has posed a threat to the power grid. When an attacker has access to multiple IoT devices within the same geographical location, they can possibly disrupt the power grid by regulating a botnet of high-wattage IoT devices. Based on the time and situation of the attack, an adversary needs access to a fixed number of IoT devices to synchronously switch on/off all of them, resulting in an imbalance between the supply and demand. When the frequency of the power generators drops below a threshold value, it can lead to the generators …
A Policy Mechanism For Federal Recommendation Of Security Standards For Mobile Devices That Conduct Transactions, Ariel Huckabay
A Policy Mechanism For Federal Recommendation Of Security Standards For Mobile Devices That Conduct Transactions, Ariel Huckabay
Electronic Theses and Dissertations
The proliferation of mobile devices in the BRIC countries has prompted them to develop policies to manage the security of these devices. In China, mobile devices are a primary tool for payments. As a result, China instituted in 2017 a cyber security policy that applies to mobile devices giving China broad authority to manage cyber threats. The United States has a similar need for a cyber policy. Mobile devices are likely to become a primary payment tool in the United States soon. DHS has also identified a need for more effective security policy in mobile devices for government operations. This …
Developing An Affect-Aware Rear-Projected Robotic Agent, Ali Mollahosseini
Developing An Affect-Aware Rear-Projected Robotic Agent, Ali Mollahosseini
Electronic Theses and Dissertations
Social (or Sociable) robots are designed to interact with people in a natural and interpersonal manner. They are becoming an integrated part of our daily lives and have achieved positive outcomes in several applications such as education, health care, quality of life, entertainment, etc. Despite significant progress towards the development of realistic social robotic agents, a number of problems remain to be solved. First, current social robots either lack enough ability to have deep social interaction with human, or they are very expensive to build and maintain. Second, current social robots have yet to reach the full emotional and social …
Development Of A Locomotion And Balancing Strategy For Humanoid Robots, Emile Bahdi
Development Of A Locomotion And Balancing Strategy For Humanoid Robots, Emile Bahdi
Electronic Theses and Dissertations
The locomotion ability and high mobility are the most distinguished features of humanoid robots. Due to the non-linear dynamics of walking, developing and controlling the locomotion of humanoid robots is a challenging task. In this thesis, we study and develop a walking engine for the humanoid robot, NAO, which is the official robotic platform used in the RoboCup Spl. Aldebaran Robotics, the manufacturing company of NAO provides a walking module that has disadvantages, such as being a black box that does not provide control of the gait as well as the robot walk with a bent knee. The latter disadvantage, …
Implementation Of An Air Supply Unit Control Scheme For The Uc2av (Unmanned Circulation Control Aerial Vehicle), Cameron Rosen
Implementation Of An Air Supply Unit Control Scheme For The Uc2av (Unmanned Circulation Control Aerial Vehicle), Cameron Rosen
Electronic Theses and Dissertations
The expanded prevalence of Unmanned Aerial Vehicles (UAVs) in recent years has created many opportunities to research novel applications for their use, enabled by the reduced cost, mission flexibility, and reduced risk that small-scale unmanned platforms provide in comparison to larger aircraft. Despite the versatility of unmanned aviation, limitations on payload size and weight, fuel and power capacity, and takeoff and landing infrastructure can restrict UAV applications, and have created a need for lift augmenting technologies that can reduce the impact of these limitations. Circulation Control (CC) is an active flow technique that has been proven as a method for …
Fail-Safe Test Generation Of Safety Critical Systems, Salwa Elakeili
Fail-Safe Test Generation Of Safety Critical Systems, Salwa Elakeili
Electronic Theses and Dissertations
This dissertation introduces a technique for testing proper failure mitigation in safety critical systems. Unlike other approaches which integrate behavioral and failure models, and then generate tests from the integrated model, we build safety mitigation tests from an existing behavioral test suite, using an explicit mitigation model for which we generate mitigation paths which are then woven at selected failure points into the original test suite to create failure-mitigation tests (safety mitigation test).
Human Action Recognition Via Fused Kinematic Structure And Surface Representation, Salah R. Althloothi
Human Action Recognition Via Fused Kinematic Structure And Surface Representation, Salah R. Althloothi
Electronic Theses and Dissertations
Human action recognition from visual data has remained a challenging problem in the field of computer vision and pattern recognition. This dissertation introduces a new methodology for human action recognition using motion features extracted from kinematic structure, and shape features extracted from surface representation of human body. Motion features are used to provide sufficient information about human movement, whereas shape features are used to describe the structure of silhouette. These features are fused at the kernel level using Multikernel Learning (MKL) technique to enhance the overall performance of human action recognition. In fact, there are advantages in using multiple types …
Reducing Communication Delay Variability For A Group Of Robots, Goncalo Martins
Reducing Communication Delay Variability For A Group Of Robots, Goncalo Martins
Electronic Theses and Dissertations
A novel architecture is presented for reducing communication delay variability for a group of robots. This architecture relies on using three components: a microprocessor architecture that allows deterministic real-time tasks; an event-based communication protocol in which nodes transmit in a TDMA fashion, without the need of global clock synchronization techniques; and a novel communication scheme that enables deterministic communications by allowing senders to transmit without regard for the state of the medium or coordination with other senders, and receivers can tease apart messages sent simultaneously with a high probability of success. This approach compared to others, allows simultaneous communications without …
Multiple Bounding Boxes Algorithm In Collision Detection And Its Performances In Sequential Vs Cuda Parallel Processing, Min Qi
Electronic Theses and Dissertations
The traditional method for detecting collisions in a 2D computer game uses a axisaligned bounding box around each sprite, and checks to determine if the bounding boxes overlap periodically. Using this single bounding box method may result in a large amount of pixel intersection tests, since a sprite may be composed of areas where the pixels are empty and the intersecting bounding box test results in false positives.
Our algorithm analysis shows that the optimal two or three bounding boxes is the best partition we can get for a reasonable time complexity. The results further show significantly diminishing returns for …
New Open Source Software For Building Molecular Dynamics Systems, Bruce Michael Allen
New Open Source Software For Building Molecular Dynamics Systems, Bruce Michael Allen
Electronic Theses and Dissertations
The context of this work is the development of open source software to support researchers to quickly build systems of molecules for molecular dynamics (MD) simulations. The goal is achieved through the integration of three open source programs by judicious modifications and creation of new source code, which allows the creation of molecular models, MD cells and the LAMMPS geometry input files. The software changes work together supporting an easy and intuitive process for simulation system creation. Creation of multiple MD cells for research simulations becomes quicker and provides needed standardization to the simulation process. The researcher can select from …
Simulation, Application, And Resilience Of An Organic Neuromorphic Architecture, Made With Organic Bistable Devices And Organic Field Effect Transistors, Robert A. Nawrocki
Simulation, Application, And Resilience Of An Organic Neuromorphic Architecture, Made With Organic Bistable Devices And Organic Field Effect Transistors, Robert A. Nawrocki
Electronic Theses and Dissertations
This thesis presents work done simulating a type of organic neuromorphic architecture, modeled after Artificial Neural Network, and termed Synthetic Neural Network, or SNN. The first major contribution of this thesis is development of a single-transistor-single-organic-bistable-device-per-input circuit that approximates behavior of an artificial neuron. The efficacy of this design is validated by comparing the behavior of a single synthetic neuron to that of an artificial neuron as well as two examples involving a network of synthetic neurons. The analysis utilizes electrical characteristics of polymer electronic elements, namely Organic Bistable Device and Organic Field Effect Transistor, created in the laboratory at …
Routing In The Dark: Pitch Black, Nathan S. Evans
Routing In The Dark: Pitch Black, Nathan S. Evans
Electronic Theses and Dissertations
In many networks, such as mobile ad-hoc networks and friend-to-friend overlay networks, direct communication between nodes is limited to specific neighbors. Friendto-friend “darknet” networks have been shown to commonly have a small-world topology; while short paths exist between any pair of nodes in small-world networks, it is non-trivial to determine such paths with a distributed algorithm. Recently, Clarke and Sandberg proposed the first decentralized routing algorithm that achieves efficient routing in such small-world networks.
Herein this thesis we discuss the first independent security analysis of Clarke and Sandberg’s routing algorithm. We show that a relatively weak participating adversary can render …